QTL mapping of the genetic basis of stem diameter in soybean.

MAIN CONCLUSION: QTL mapping of stem diameter was carried out in three RIL populations using a high-density genetic map, and candidate genes related to stem diameter were predicted. Stem diameter is an important agronomic trait affecting soybean lodging and productivity. However, this trait is underexploited, and the underlying genetic mechanism in soybean remains unclear. In this study, three recombinant inbred line (RIL) populations, including 156 F10 lines from Nannong 94-156 × Bogao (N × B), 127 F9 lines from Dongnong 50 × Williams 82 (D × W), and 146 F9 lines from Suinong 14 × Enrei (S × E), were used to identify QTLs for soybean stem diameter across multiple environments. Phenotype analysis revealed that stem diameter exhibited strong positive correlations with plant height and 100-seed weight, two of the most important yield components. A total of 12 QTLs for stem diameter were identified on eight chromosomes across three RIL populations and five environments. The most influential QTL that was stably identified across all the populations and environments, q11, explained 12.58-26.63% of the phenotypic variation. Detection of several environment-specific QTLs, including q14, q16, and q20, suggests that environments may also have important effects in shaping the natural variation in soybean stem diameter. Furthermore, we predicted candidate genes underlying the QTLs and found that several promising candidate genes may be responsible for the variation in stem diameter in soybean. Overall, the markers/genes linked closely or underlying the major QTLs may be used for marker-assisted selection of soybean varieties to enhance lodging resistance and even yield. Our results lay the foundation for the fine mapping of stem development-related genes to reveal the molecular mechanisms.

Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients.

OBJECTIVE: This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. METHODS: A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. RESULTS: The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84-0.91). CONCLUSION: CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.